Insights into the source contributions to the elevated fine particulate matter in Nigeria using a source-oriented chemical transport model.

In 2021, Nigeria was ranked by the World Health Organization (WHO) as one of the top countries with highly deteriorating air quality in the world. To date, no study has elucidated the sources of elevated fine particulate matter (PM2.5) concentrations over the entire Nigeria. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to quantify the contributions of seven emissions sectors to PM2.5 and its components in Nigeria in 2021. Residential, industry, and agriculture were the major sources of primary PM (PPM) during the four seasons, elemental carbon (EC) and primary organic carbon (POC) were dominated by residential and industry, while residential, industry, transportation, and agriculture were the important sources of secondary inorganic aerosols (SIA) and its components in most regions. PM2.5 was up to 150 µg/m3 in the north in all the seasons, while it reached ∼80 µg/m3 in the south in January. Residential contributed most to PM2.5 (∼80 µg/m3), followed by industry (∼40 µg/m3), transportation (∼20 µg/m3), and agriculture (∼15 µg/m3). The large variation in the sources of PM2.5 and its components across Nigeria suggests that emissions control strategies should be separately designed for different regions. The results imply that urgent control of PM2.5 pollution in Nigeria is highly necessitated.

Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS).

Biomass fuel burning is a significant contributor of household fine particulate matter (PM2.5) in the low to middle income countries (LMIC) and assessing PM2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM2.5 concentrations than those using electricity/liquefied petroleum gas (626 µg/m3 vs. 213 µg/m3). Exposure model performances showed 10-fold cross validated R2 ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM2.5 from the third wave of monitoring and ambient PM2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM2.5 included ambient PM2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM2.5 with moderate to high confidence using ambient PM2.5 and household characteristics. Our results present a framework for estimating household PM2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

Long-term PM2.5 pollution over China: Identification of PM2.5 pollution hotspots and source contributions.

Fine particulate matter, with an aerodynamic diameter ≤ 2.5 µm (PM2.5), is a severe problem in China. The lack of ground-based measurements and its sparse distribution obstruct long-term air pollution impact studies over China. Therefore, the present study used newly updated Global Estimates (V5. GL.02) of monthly PM2.5 data from 2001 to 2020 based on Geographically Weighted Regression (GWR) by Washington University. The GWR PM2.5 data were validated against ground-based measurements from 2014 to 2020, and the validation results demonstrated a good agreement between GWR and ground-based PM2.5 with a higher correlation (r = 0.95), lower error (8.14), and lower bias (-3.10 %). The long-term (2001-2020) PM2.5 data were used to identify pollution hotspots and sources across China using the potential source contribution function (PSCF). The results showed highly significant PM2.5 pollution hotspots in central (Henan, Hubei), North China Plain (NCP), northwest (Taklimakan), and Sichuan Basin (Chongqing, Sichuan) in China, with the most severe pollution occurring in winter compared to other seasons. During the winter, PM2.5 was in the range from 6.08 to 93.05 µg/m3 in 33 provinces, which is 1.22 to 18.61 times higher than the World Health Organization (WHO) Air Quality Guidelines (AQG-2021; annual mean: 5 µg/m3). In 26 provinces, the reported PM2.5 was 1.07 to 2.66 times higher than the Chinese Ambient Air Quality Standard (AAQS; annual mean: 35 µg/m3). Furthermore, provincial-level trend analysis shows that in most Chinese provinces, PM2.5 increased significantly (3-43 %) from 2001 to 2012, whereas it decreased by 12-94 % from 2013 to 2020 due to the implementation of air pollution control policies. Finally, the PSCF analysis demonstrates that China's air quality is mainly affected by local PM2.5 sources rather than by pollutants imported from outside China.

Comment on Abdul Jabbar et al. Air Quality, Pollution and Sustainability Trends in South Asia: A Population-Based Study. Int. J. Environ. Res. Public Health 2022, 19, 7534.

This comment discusses the use of PM2.5 (mass concentration of fine particulate matter with an aerodynamic diameter less than 2.5 microns) data in the recently published article entitled "Air Quality, Pollution and Sustainability Trends in South Asia: A Population-Based Study" by Abdul Jabbar et al. [...].

Three-dimensional nature of summertime aerosols over South Asia.

Three-dimensional (temporal-spatial-vertical) climatology of South Asian summertime (MAMJ, 2010-2019) aerosols and aerosol sub-types was explored using multiple high-resolution satellite-based observations and reanalysis dataset. Vertical stratification of aerosol layer and aerosol sub-types was identified using observation from space-borne lidar. Aerosol optical depth (AOD) was particularly high across the Indo-Gangetic Plain (IGP; AOD ± SD: 0.56 ± 0.12) and over eastern coast of India (AOD: 0.6-0.8), with prevalence of heterogeneous aerosol sub-types having strong spatial gradient. Clearly, aerosols over north-western arid part were highly absorbing (Ultra-violet Aerosol Index, UVAI > 0.80) and coarse (Ångström exponent, AE < 0.8), with an indication of desert/-mineral dust aerosols. In contrast, fine and moderate to non-absorbing aerosols (UVAI: 0.20-0.50) dominate from central to lower IGP, including in Bangladesh, with signature of anthropogenic emissions. Prevailing aerosols over twelve South Asian cities were classified into six aerosol sub-types constraining their particle size and UV-absorbing potential. Overall, mineral dust, smoke and urban aerosols were the three major aerosol sub-types that prevail across South Asia during summer. In particular, 58-70 % of retrieval days over Karachi and Multan were dust dominated; 57-64 % days were dust or urban aerosols dominated over Lahore, Delhi, Kanpur and Varanasi, and 56-77 % days were smoke or urban aerosols dominated over Dhaka, Kathmandu, Chennai, Mumbai, Colombo and Nagpur. Prevailing aerosols were vertically stratified as 50-70 % of total AOD was retrieved <2 km from the surface except in few cities where 70-80 % of AOD was retrieved <3 km height. Mineral dust and/or urban aerosols emerged as the most abundant aerosol types near the surface (<1 km) in all the cities except in Chennai, with their abundance remained as a function of emission sources and geographical location.

Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data.

In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD550 nm) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua satellites during the years 2003-2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh. The annual mean AOD from both CAMS and MODIS DTB is high (>0.60) over most parts of Bangladesh except for the eastern areas of Chattogram and Sylhet. Higher AOD is observed in spring and winter than in summer and autumn, which is mainly due to higher local anthropogenic emissions during the winter to spring season. Annual trends from 2003-2020 show a significant increase in AOD (by 0.006-0.014 year-1) over Bangladesh, and this increase in AOD was more evident in winter and spring than in summer and autumn. The increasing total AOD is caused by rising anthropogenic emissions and accompanied by changes in aerosol species (with increased OC, sulfate, and BC). Overall, this study improves understanding of aerosol pollution in Bangladesh and can be considered as a supportive document for Bangladesh to improve air quality by reducing anthropogenic emissions.

Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance (R2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 µg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 µg/m3.

Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain.

Attenuated backscatter profiles retrieved by the space borne active lidar CALIOP on-board CALIPSO satellite were used to measure the vertical distribution of smoke aerosols and to compare it against the ECMWF planetary boundary layer height (PBLH) over the smoke dominated region of Indo-Gangetic Plain (IGP), South Asia. Initially, the relative abundance of smoke aerosols was investigated considering multiple satellite retrieved aerosol optical properties. Only the upper IGP was selectively considered for CALIPSO retrieval based on prevalence of smoke aerosols. Smoke extinction was found to contribute 2-50% of the total aerosol extinction, with strong seasonal and altitudinal attributes. During winter (DJF), smoke aerosols contribute almost 50% of total aerosol extinction only near to the surface while in post-monsoon (ON) and monsoon (JJAS), relative contribution of smoke aerosols to total extinction was highest at about 8 km height. There was strong diurnal variation in smoke extinction, evident throughout the year, with frequent abundance of smoke particles at lower height (<4 km) during daytime compared to higher height during night (>4 km). Smoke injection height also varied considerably during rice (ON: 0.71 ±â€¯0.65 km) and wheat (AM: 2.34 ±â€¯1.34 km) residue burning period having a significant positive correlation with prevailing PBLH. Partitioning smoke AOD against PBLH into the free troposphere (FT) and boundary layer (BL) yield interesting results. BL contribute 36% (16%) of smoke AOD during daytime (nighttime) and the BL-FT distinction increased particularly at night. There was evidence that despite travelling efficiently to FT, major proportion of smoke AOD (50-80%) continue to remain close to the surface (<3 km) thereby, may have greater implications on regional climate, air quality, smoke transport and AOD-particulate modelling.

Attributing mortality from temperature extremes: A time series analysis in Varanasi, India.

Climate extremes are often associated with increased human mortality and such association varies considerably with space and time. We therefore, aimed to systematically investigate the effects of temperature extremes, daily means and diurnal temperature variations (DTV) on mortality in the city of Varanasi, India during 2009-2016. Time series data on daily mortality, air quality (SO2, NO2, O3 and PM10) and weather variables were obtained from the routinely collected secondary sources. A semiparametric quasi-Poisson regression model estimated the effects of temperature extremes on daily all-cause mortality adjusting nonlinear confounding effects of time trend, relative humidity and air pollution; stratified by seasons. An effect modification by age, gender and place of death as semi-economic indicator were also explored. Daily mean temperature was strongly associated with excess mortality, both during summer (5.61% with 95% CI: 4.69-6.53% per unit increase in mean temperature) and winter (1.53% with 95% CI: 0.88-2.18% per unit decrease in mean temperature). Daily mortality was found to be increased by 12.02% (with 95% CI: 4.21-19.84%) due to heat wave. The DTV has exhibited downward trend over the years and showed a negative association with all-cause mortality. Significant association of mortality and different metric of temperature extreme along with decreasing trend in DTV clearly indicate the potential impact of climate change on human health in the city of Varanasi. The finding may well be useful to prioritize the government policies to curb the factors that causes the climate change and for developing early warning system.

Airborne particulate in Varanasi over middle Indo-Gangetic Plain: variation in particulate types and meteorological influences.

The variation in particulate mass and particulate types (PM2.5 and PM10) with respect to local/regional meteorology was analyzed from January to December 2014 (n = 104) for an urban location over the middle Indo-Gangetic Plain (IGP). Both coarser (mean ± SD; PM10 161.3 ± 110.4 µg m-3, n = 104) and finer particulates (PM2.5 81.78 ± 66.4 µg m-3) revealed enormous mass loading with distinct seasonal effects (range: PM10 12-535 µg m-3; PM2.5 8-362 µg m-3). Further, 56% (for PM2.5) to 81% (for PM10) of monitoring events revealed non-attainment national air quality standard especially during winter months. Particulate types (in terms of PM2.5/PM10 0.49 ± 0.19) also exhibited temporal variations with high PM2.5 loading particularly during winter (0.62) compared to summer months (0.38). Local meteorology has clear distinguishing trends in terms of dry summer (March to June), wet winter (December to February), and monsoon (July to September). Among all the meteorological variables (average temperature, rainfall, relative humidity (RH), wind speed (WS)), temperature was found to be inversely related with particulate loading (rPM10 -0.79; rPM2.5 -0.87) while RH only resulted a significant association with PM2.5 during summer (rPM10 0.07; rPM2.5 0.55) and with PM10 during winter (rPM10 0.53; rPM2.5 0.24). Temperature, atmospheric boundary layer (ABL), and RH were cumulatively recognized as the dominant factors regulating particulate concentration as days with high particulate loading (PM2.5 >150 µg m-3; PM10 >260 µg m-3) appeared to have lower ABL (mean 660 m), minimum temperature (<22.6 °C), and high RH (∼79%). The diurnal variations of particulate ratio were mostly insignificant except minor increases during night having a high wintertime ratio (0.58 ± 0.07) over monsoon (0.34 ± 0.05) and summer (0.30 ± 0.07). Across the region, atmospheric visibility appeared to be inversely associated with particulate (rPM2.5 -0.84; rPM10 -0.79) for all humid conditions, while at RH ≥80%, RH appeared as the most dominant factor in regulating visibility compared to particulate loading. The Lagrangian particle dispersion model was further used to identify possible regions contributing particulate loading through regional/transboundary movement.